Portrait of Dhanya Sridhar

Dhanya Sridhar

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Causality
Deep Learning
Probabilistic Models
Reasoning
Representation Learning

Biography

Dhanya Sridhar is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

She was a postdoctoral researcher at Columbia University and received her doctorate from the University of California, Santa Cruz.

In brief, Sridhar’s research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently and discover new knowledge alongside us.

Current Students

PhD - Université de Montréal
Co-supervisor :
Collaborating researcher - Helmholtz AI
Independent visiting researcher - University of Maryland College Park
PhD - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :

Publications

Heterogeneous Supervised Topic Models
Hal Daumé III
David Blei
Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to bo… (see more)th uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.